Data Engineer - £350PD - Remote

City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer - £350PD - Remote

Required Technical Skills

Data Pipeline & ETL

Design, build, and maintain robust ETL/ELT pipelines for structured and unstructured data

Hands-on experience with AWS Glue and AWS Step Functions

Implementation of data validation, data quality frameworks, and reconciliation checks

Strong error handling, monitoring, and retry strategies in production pipelines

Experience with incremental data processing patterns (CDC, watermarking, upserts)

AWS Data Services

Amazon S3: data lake architectures, partitioning strategies, lifecycle policies

DynamoDB: data modeling, secondary indexes, streams, and performance optimization

Amazon Redshift: foundational querying, integrations, and performance considerations

AWS Lambda for scalable data processing and orchestration

Amazon EventBridge for event-driven and decoupled data pipelines

Vector Databases & Embeddings

Strong understanding of vector database concepts, indexing strategies, and performance trade-offs

Design and implementation of embedding generation pipelines

Optimization techniques for semantic search and retrieval accuracy

Effective chunking strategies for document ingestion and processing

Experience with CockroachDB deployment and management is beneficial

Document Processing

Experience with PDF parsing libraries such as PyPDF2, pdfplumber, and AWS Textract

Integration of OCR solutions (AWS Textract, Tesseract) for scanned documents

Extraction of document structure (headings, tables, sections)

Metadata extraction, normalization, and enrichment

Handling of multiple document formats including PDF, HTML, and DOCX

Data Integration

Familiarity with SAP data structures is beneficial

Integration with PIM (Product Information Management) systems

Design and consumption of REST APIs

Programming & Querying

Python (advanced): pandas, numpy, boto3, and data processing best practices

SQL (advanced): complex queries, performance tuning, and query optimization

Data Quality & Governance

Data profiling and ongoing quality assessment

Schema validation and evolution strategies

Data lineage tracking and observability

Understanding of Master Data Management (MDM) concepts

Domain Knowledge

Product catalog data models and hierarchies

E-commerce data patterns and integrations

B2B data exchange and system integration

To apply for this role please submit your CV or contact Dillon Blackburn on (phone number removed) or at (url removed).

Tenth Revolution Group are the go-to recruiter for Data & AI roles in the UK offering more opportunities across the country than any other recruitment agency. We're the proud sponsor and supporter of SQLBits, Power Platform World Tour, and the London Fabric User Group. We are the global leaders in Data & AI recruitment

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.